Systems And Methods For Evaluating The Brain After Onset Of A Stroke Using Computed Tomography Angiography

ABSTRACT

In one embodiment, a patient&#39;s brain is evaluated after onset of a stroke by capturing computed tomography angiography (CTA) images of the brain, analyzing the CTA images with a CTA image analysis program to evaluate the patient&#39;s brain, and generating results based upon the analysis that provide an assessment of the brain. In some cases, the CTA image analysis program comprises a machine-learning algorithm that has been trained on the results of perfusion imaging analysis.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to co-pending U.S. ProvisionalApplication Ser. No. 62/731,108, filed Sep. 14, 2018, which is herebyincorporated by reference herein in its entirety.

BACKGROUND

Endovascular stroke therapy (EST) is one of the most significantadvances in acute ischemic stroke (AIS) care in recent history. ESTinvolves the physical removal of a thrombus from an artery in the brainto restore blood flow to ischemic brain tissue. Recent trials havedemonstrated that patient disability after stroke can be reduced if ESTis performed on an eligible patient within the first 24 hours of strokesymptoms. Accordingly, it is critical to determine within that timeperiod whether or not a stroke patient is a candidate for EST.

The preferred method of evaluating the brain for purposes of determiningwhether or not to perform EST is to perform perfusion imaging (PI),which typically involves analyzing computed tomography perfusion (CTP)or magnetic resonance imaging (MRI) images. While PI images can be usedto determine the amount of dead brain tissue and brain tissue at risk ofdying, most hospitals do not have the capability to perform PI 24 hoursa day as it requires specialized equipment, software, and training. As aconsequence, stroke patients who could benefit from EST are not alwaystreated with such therapy.

In view of the above discussion, it can be appreciated that there is aneed for a way to evaluate the brain after onset of a stroke todetermine eligibility for EST without having to perform PI.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure may be better understood with reference to thefollowing figures. Matching reference numerals designate correspondingparts throughout the figures, which are not necessarily drawn to scale.The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is schematic view of an embodiment of a system for evaluating thebrain after onset of a stroke based on computed tomography angiography(CTA).

FIG. 2 is a block diagram of an embodiment of a computing device shownin FIG. 1.

FIG. 3 is a flow diagram of an embodiment of a method for training a CTAimage analysis program.

FIG. 4 is an example output of a computed tomography (CT) perfusionanalysis program.

FIG. 5 is a flow diagram of an embodiment of a method for analyzing CTAimages to evaluate the brain after onset of a stroke.

FIG. 6 is a schematic diagram that shows the architecture of the novelDeepSymNet convolutional neural network and its training based uponsymmetry-sensitive relationships.

FIG. 7 includes multiple CTA images that identify DeepSymNet activationzones for a large vessel occlusion (LVO) identification task. Colormapping indicates the magnitude of the contribution to the LVOdetermination.

FIG. 8A is a graph that plots receiver-operating sensitivity curves forthe detection of LVO using DeepSymNet.

FIG. 8B is a graph that plots receiver-operating precision curves forthe detection of LVO using DeepSymNet.

FIG. 9A is a graph that plots curves and correspondingareas-under-the-curve (AUCs) for the DeepSymNet performance inidentifying infarct core 30 mL (DAWN Criteria 1) and infarct core 50 mL(Dawn Criteria 2) as determined by CTP-RAPID®.

FIG. 9B is a graph that plots lines demonstrating the improvingperformance of DeepSymNet to identify ischemic cores by ischemic corevolume. The dark green line represents a point estimate and the shadedgreen area represents the 95% confidence interval (CI).

FIG. 9C is a graph that plots curves and corresponding AUCs for theDeepSymNet performance in identifying infarct cores 30 mL (DAWNCriteria 1) and infarct cores 50 mL (Dawn Criteria 2) in patientspresenting in 6 hours.

FIG. 9D is a graph that plots curves and corresponding AUCs for theDeepSymNet performance in identifying infarct cores 30 mL (DAWNCriteria 1) and infarct cores 50 mL (Dawn Criteria 2) in patientspresenting in >6 hours and <24 hours from the last known well time.

DETAILED DESCRIPTION

As described above, there is a need for a way to evaluate the brainafter onset of a stroke to determine eligibility for endovascular stroketherapy (EST) without having to perform perfusion imaging (PI), whichtypically requires use of computed tomography perfusion (CTP) ormagnetic resonance imaging (MRI). More particularly, there is a need fora way to perform such an evaluation based upon images captured using animaging protocol that is more commonly available at most hospitals.Disclosed herein are systems and methods for evaluating the brain afteronset of a stroke and, therefore, determining eligibility for EST, thatare based on computed tomography angiography (CTA) images. As mosthospitals are able to perform CTA, the disclosed systems and methodsenable more patients to be evaluated within the 24-hour critical periodand, therefore, enable more eligible patients to receive EST. In someembodiments, a system for evaluating the brain after onset of a strokecomprises a computer program that analyzes CTA images to assess variousparameters associated with the stroke and the patient's eligibility forEST. With such information, a medical professional (e.g., surgeon) canmake a determination as to whether or not EST should be performed. Insome embodiments, the program leverages a machine-learning algorithmthat has been trained using the results of PI image analysis software.In such a case, results comparable to those based upon PI images can beobtained using CTA images.

In the following disclosure, various specific embodiments are described.It is to be understood that those embodiments are exampleimplementations of the disclosed inventions and that alternativeembodiments are possible, including alternative embodiments that includefeatures from different embodiments. All such embodiments are intendedto fall within the scope of this disclosure.

FIG. 1 illustrates an example system 10 for evaluating the brain afteronset of a stroke using CTA. As shown in the figure, the system 10generally includes a computed tomography (CT) machine 12 that is capableof capturing CTA images and a computing device 14 in electricalcommunication with the CT machine that can control operation of themachine and perform analysis on CTA images captured by the machine toassess an ischemic core and related phenomena.

FIG. 2 is a block diagram of an example embodiment for the computingdevice 14 shown in FIG. 1. The computing device 14 generally comprises aprocessing device 16, memory 18, a user interface 20, and one or moreinput/output (I/O) devices 22, each of which being connected to a systembus 24. The processing device 16 can, for example, include a centralprocessing unit (CPU) that is capable of executing computer-executableinstructions stored within the memory 18. The memory 18 can include anyone of or a combination of volatile memory elements (e.g., RAM, flash,etc.) and nonvolatile memory elements (e.g., hard disk, ROM, etc.).

The user interface 20 can comprise one or more devices that can enteruser inputs into the computing device 14, such as a keyboard and mouse,as well as one or more devices that can convey information to the user,such as a display. The I/O devices 22 can comprise components thatenable the computing device 14 to communicate with other devices, suchas a network adapter and a wireless transceiver. While particularcomponents are illustrated in FIG. 2, it is noted that the computingdevice 14 need not comprise each of these components and can compriseother components. For example, the computing device 14 can furthercomprise a graphical processing device, such as a graphical processingunit (GPU).

In the illustrated example, the memory 18 (a non-transitorycomputer-readable medium) stores executable programs (software)including an operating system 26, a CTA control program 28, and a CTAimage analysis program 30. Each of the programs includescomputer-executable instructions, which may be comprised by one or morealgorithms (i.e., computer logic), that can be executed by theprocessing device 16. The operating system 26 controls the generaloperation of the computing device 14, while the CTA control program 28controls operation of the CT machine 12 and receives CTA images that canbe analyzed by the CTA image analysis program 30. The CTA image analysisprogram 30 is configured to analyze CTA images to evaluate the brainafter onset of a stroke and provide information that may be relevant tothe determination as to whether or not EST is advisable for the patient.In some embodiments, the CTA image analysis program 30 incorporates amachine-learning algorithm that has been trained using the results of PIimage analysis so that the CTA image analysis program is capable ofgenerating results comparable to those that would be generated byexisting PI analysis software.

FIG. 3 is a flow diagram that illustrates an example method for trainingthe CTA image analysis program so that it can evaluate the brain afteronset of a stroke in a manner similar to PI analysis software. Beginningwith block 40, both PI and CTA images are captured for a given strokepatient within 24 hours of the onset of stroke symptoms. In someembodiments, capturing PI images comprises capturing multiple PI scans,each scan comprising multiple images that represent slices of the brain,over a predetermined period of time as an injected contrast agent flowsthrough the cerebral arteries. In some embodiments, capturing CTA imagescomprises capturing a single scan, also comprising multiple images thatrepresent slices of the brain, when an injected contrast agent is withinthe cerebral arteries. Regardless, the two sets of images (i.e., a PIimage set and a CTA image set) are acquired close in time to each otherso as to enable correlation between the images and the results theyreveal.

Referring next to block 42, post-processing can be performed on both thePI and CTA images to remove any undesired anatomical features present inthe images. Such features can, for example, comprise bones and bloodvessels, although, as described below, the latter may be useful inperforming the analysis. The processed images can then be registeredwith an atlas of the brain to identify the orientation of the brain, asindicated block 44.

With reference next to block 46, analysis can be performed on the PIimages to evaluate the brain after onset of a stroke. Various parametersassociated with the stroke and the patient's eligibility for EST can bedetermined through this analysis. Such parameters can include one ormore of the extent (e.g., volume) of an existing infarction (ischemiccore), the amount of tissue (e.g., volume) at risk of infarction, thepresence or absence of a large vessel occlusion (LVO), and the presenceor absence of brain hemorrhage. Such analysis can be performed using acommercial analysis program, such as the CTP-RAPID® software package(IschemaView, Stanford, Calif.). When this analysis is performed,results are generated that provide an assessment of the patient's brain(block 48), which assists the medical professional in making adetermination as to whether or not to perform EST.

FIG. 4 provides one example of results that can be produced by suchanalysis. In particular, FIG. 4 shows a graphical output that includes aseries of PI images of various vertical slices of the brain thatidentify both necrotic and at-risk brain tissue. In this example, the 8images on the left identify dead tissue with a pink color and the 8images on the right identify at-risk tissue with a green color. Such avisual representation gives the medical professional an idea of theamount and location of the damage as well as an idea of which and howmuch tissue could potentially be saved if EST were performed. Inaddition to these visual representations, the output shown in FIG. 4also includes estimates of the volume of dead tissue and the volume ofat-risk tissue. In this example, it is estimated that there is 0 ml ofdead brain tissue (based upon relative cerebral blood flow (CBF) beingbelow 30%) and 87 ml of at-risk brain tissue (based upon the Tmaxindicator being greater than 6 seconds). In other embodiments, theresults of the PI image analysis can additionally comprise an indicationof the presence or absence of an LVO and/or the presence or absence ofbrain hemorrhage. In still further embodiments, the results of the PIimage analysis can also include an explicit indication as to whether ornot EST is advised. Such an indication can, for example, comprise asimple “treat” or “do not treat” indication (or equivalent), or couldcomprise some form of qualitative measure as to the advisability of EST.

Irrespective of the nature of the results obtained through the PI imageanalysis, the goal is to be able to generate results comparable to thoseobtained using PI but based solely on CTA images. To that end, amachine-learning algorithm of the CTA image analysis program associatesfeatures within the CTA images with the results obtained by analyzingthe PI images, as indicated in block 50. An example machine-learningalgorithm, as well as experimental results obtained using thatalgorithm, are described in detail below. When the above-describedprocess is performed multiple times for multiple patients and multiplesets of PI and CTA images, the algorithm learns what results would bereached by analyzing PI images by analyzing the features of CTA images.Accordingly, similar results can be achieved without having to performPI.

Flow at this point depends upon whether or not training is complete. Ifnot, flow returns to block 40 and PI and CTA images are captured fromthe next stoke patient. The process continues in this manner until theCTA image analysis program is capable of generating comparable resultsto those obtained by analyzing PI images. In such a case, the CTAanalysis can be performed as a proxy for the PI analysis.

Once the CTA image analysis program has been trained, it can be used asa tool for assisting medical professionals in making determinations asto whether or not stroke patients should receive EST. FIG. 5 is a flowdiagram that illustrates the operation of the CTA image analysis programin that capacity. Beginning with block 60, CTA images are captured for anew stroke patient within 24 hours of the onset of symptoms. As notedabove, capturing CTA images can comprise capturing a single scan thatincludes multiple images (slices) when an injected contrast agentreaches the cerebral arteries. Post-processing can then be performed onthe CTA images to remove any undesired anatomical features present inthe images (block 62) and the processed images can be registered with anatlas of the brain (block 64). Although not identified in FIG. 5,further processing can also include comparing the left hemisphere of thebrain to the right hemisphere of the brain to enhance the ischemic corethat exists in one of the hemispheres. An example of this is alsodescribed below.

With reference next to block 66, analysis can be performed on the CTAimages to evaluate the patient's brain. As described above, variousparameters associated with the stroke and the patient's eligibility forEST can be determined, such as one or more of the extent (e.g., volume)of an existing infarction (ischemic core), the amount of tissue (e.g.,volume) at risk of infarction, the presence or absence of an LVO, andthe presence or absence of brain hemorrhage. These determinations arebased upon the training of the machine-learning algorithm of the CTAimage analysis program described in relation to FIG. 3. Once theanalysis has been performed, results are generated that provide anassessment of the patient's brain, as indicated in block 68. Theseresults can be similar to those described above that were obtained byanalyzing PI images. Accordingly, the results can, for example, compriseone or more of a series of CTA images of vertical slices of the brainthat identify both necrotic and at-risk tissue, an indication of thepresence or absence of an LVO, the presence or absence of brainhemorrhage, and an indication as to whether or not EST is advised.Irrespective of the particular nature results that are generated, themedical professional can then make a determination as to whether or notEST should be performed on the patient.

A novel convolutional neural network (CNN) referred to as the DeepSymmetry-Sensitive Convolutional Neural Network, or DeepSymNet, wascreated by the inventors and a study was performed at the University ofTexas Health Science Center (UTHSC) at Houston, Tex., to evaluate itsability to identify LVOs as well as infarct cores from CTA images usingCTP-RAPID® definitions. The study population comprised 2 groups ofpatients. Control patients were defined as patients who presented to theUTHSC Emergency Department for acute stroke evaluation, underwent fullneuroimaging evaluation as described below, but were ultimatelydiagnosed as not having AIS or transient ischemic attack (TIA). For thispopulation, a consecutive cohort of patients from January 2018 to March2018 was identified. The second group of patients comprised patients whowere diagnosed with AIS or TIA after evaluation by an inpatient strokeneurology service at UTHSC. These patients were identified from aprospectively maintained stroke program database from January 2016 toMarch 2018. Patients with and without LVOs were included. This databaseprospectively records all patients admitted with a stroke or TIA andcaptures demographic, clinical, radiographic, and outcome data.

All patients presenting to the Emergency Department for an acute strokeevaluation underwent the same neuroimaging protocol, which consists of anon-contrast head CT (NCHCT) followed by CTP and CTA. As such, anidentical imaging protocol was performed in patients who were ultimatelydiagnosed with LVO AIS or non-LVO AIS, as well as those ultimatelydiagnosed as not having AIS or TIA. Post-processing of the CTP imageswas performed using CTP-RAPID® and default parameters that define anischemic core as relative CBF reduction of least 30% of thecontralesional hemisphere. Imaging data was gathered in a retrospectivefashion, and CTA acquisition protocols were not altered in any way fromthe standard of care. CTAs were acquired using a standard single-phaseacquisition technique.

Because the purpose of the study was to train and validate amachine-learning approach to stroke neuroimaging, the cohort of patientsin the second group was balanced to contain comparable numbers ofpatients with small, moderate, and large-sized ischemic cores atpresentation. A total sample size of approximately 300 patients wastargeted, with roughly ⅔ of the patients having stroke and ⅓ asnon-stroke.

Patients were excluded from the study for incomplete neuroimaging. Inaddition, patients were excluded if their neuroimaging containedsignificant motion artifacts or were otherwise of inadequate quality,including large-volume old infarcts or implanted materials, such asventriculo-peritoneal shunts or aneurysm coils. Finally, patients withposterior circulation occlusions were excluded as these regions areincompletely evaluated by CTP.

Two primary end points were chosen to reflect questions that the AISneuroimaging evaluation should address. The first end point was accuracyin the detection of LVO. LVO was defined as endovascularly accessiblevessel occlusions that would be potentially amenable to EST, i.e., thosewithin the intracranial internal carotid artery, M1 and M2 segments ofthe middle cerebral artery, and A1 and A2. As mentioned above, patientswith vertebral, basilar, or posterior cerebral artery occlusions wereexcluded. The presence or absence of LVO was determined by expertinterpretation of the raw CTA images.

The second primary end point was accuracy in detection of small ischemiccores. To this end, ischemic core was defined as the CTP-RAPID®-basedischemic core measurements (relative cerebral blood flow <30%) from theCTP images. This end point was first evaluated in a dichotomous fashionand cutoffs were chosen to coincide with criteria from the DAWN trial, arecent large study examining CTP-RAPID®-predicted ischemic core-basedselection of patients for EST. As such, the 2 dichotomous end pointsconsisted of ischemic cores ≤30 mL and ischemic cores ≤50 mL. Insensitivity analysis, these end points were examined in patientspresenting in the early time window (0-6 hours) and late time window(6-24 hours) separately. Finally, the ability of the machine-learningmethod to predict CTP-RAPID® ischemic core volume determinations as acontinuous variable using correlation analysis was also evaluated.

DeepSymNet was designed to leverage brain symmetry information to learna particular outcome variables, for example, the presence of LVO andischemic core volume. DeepSym Net analyzes the symmetry information inCTA images of the brain to determine if the patient has suffered fromAIS, without needing the location of the areas affected. Inspired fromthe observation that the two hemispheres of the brain are visiblydifferent in CTA images both in terms of vasculature structures as wellas voxel intensities in the tissue affected by the stroke, the networkrobustly compares the two hemispheres of the patient's brain, both inthe presence and in the absence of blood vessels in the images, toidentify if a patient has suffered from AIS. Qualitative analysis of thenetwork is also performed by visualizing the network activation for CTAimages of AIS patients. The analysis confirms that the network learns toidentify blood vessels and tissue structures in one hemisphere of thebrain that are not present in the other hemisphere, thereby indicating adiagnosis of AIS. The mathematical basis for DeepSimNet is described inparagraphs that follow.

Given brain CTA volumes, V_(i), with labels, C_(i)ϵ{0,1}, i=1, 2; . . ., n, the goal is to learn a mapping V_(i)→C_(i) that correctlyclassifies a brain CTA volume as having a stroke (C_(i)=1) or no stroke(C_(i)=0). DeepSymNet learns the asymmetry between the CTA volumes ofthe two hemispheres of a brain in order to detect AIS. This approach isinspired from the clinical observation that, typically, in the event ofan ischemic stroke, blood vessels in one hemisphere of the brain arelikely to be occluded while those in the other hemisphere are lessaffected. Consequently, observable differences in vasculature and braintissue structure are seen in the two hemispheres of brain from CTAimages.

An inception module is a combination of parallel convolutional layersthat can be used as a building block for constructing deep neuralnetworks, which have the ability to learn complex patterns in data. Themodule uses 1×1, 3×3, and 5×5 convolutions, along with max pooling,enabling the network to learn patterns at different scales from thedifferent filter sizes used in parallel. Here, three-dimensionalinception modules (i.e., three-dimensional versions of the filters:convolutions with kernels of size 1×1×1, 3×3×3, and 5×5×5) andthree-dimensional max pooling are used since the data comprisesthree-dimensional volumes. The relationship between the input, T_(IM),and output, O_(IM), of an inception module is given by,

_(IM)={ƒ₁ ^(n)⊗

_(IM)}⊕{ƒ₃ ^(n)⊗(ƒ₁ ^(n)⊗

_(IM))}⊕{ƒ₅ ^(n)⊗(ƒ₁ ^(n)⊗

_(IM))}⊕{ƒ₁ ^(n)⊗

₃ ^(max)(

_(IM))}  (1)

where ƒ^(n) _(k) denotes n convolutional filters of kernel size k alongeach dimension, P^(max) _(k) denotes max pooling with kernel size kalong each dimension, ⊗ denotes convolution, and ⊕ denotes concatenationof filter outputs of same dimensions.

A Siamese Network is a neural network architecture that uses identicalneural networks with identical weights to learn the similarities anddifferences between two inputs. Originally proposed for signatureverification, it has been used for various computer vision applications.Although most networks employ a cost function to compare the outputs ofthe two identical networks for different inputs, a significantlydifferent approach has been used here that employs convolutional layersto learn the complex differences in structure that are embedded in theoutput of the identical networks learning the differences and theaddition of an L−1 merge layer.

DeepSymNet is designed to take the CTA images of the two hemispheres ofthe brain as its inputs. The right hemispheres were flipped tofacilitate the network learning process. The architecture of DeepSym Netis shown in FIG. 6.

Two identical convolutional neural networks having identical weightswere used for learning the low- and high-level volume three-dimensionalrepresentations common to the two brain hemispheres. This convolutionalneural network architecture employs 4 three-dimensional inceptionmodules, one after the other. Then, instead of the commonly usedapproach of directly comparing differences with a cost function (such ascontrastive loss), a markedly different method is used. Specifically, a“merge layer” was created that calculates the absolute difference (L−1difference) between the high-level convolution filter outputs common tothe two hemispheres. These differences contain crucial information aboutthe asymmetry of the two hemispheres that cannot be accounted for usinga simple loss function. Hence, the network is enabled to learn theinformation in the hemisphere differences by adding two additionalinception modules to the architecture. The outputs from the finalinception module are then max pooled and connected to the outputprediction layer through a fully connected layer. The L−1 merge layerenables the network to equally weigh stroke visual patterns generatedfrom the left or right hemisphere, and further enables the additionalinception modules to learn convolutional filters sensitive to high-leveldifferences.

In the model, 64 filters of each size were used in each of the inceptionmodules. The entire network uses ReLU activations, except for theprediction layer that uses sigmoid activation. Finally, the lossfunction used is binary cross-entropy, which is given by,

$\begin{matrix}{{J\left( {y,\hat{y}} \right)} = {- {\sum\limits_{i}\left\{ {{y_{i}\log \; {\hat{y}}_{i}} + {\left( {1 - y_{i}} \right){\log \left( {1 - {\hat{y}}_{i}} \right)}}} \right\}}}} & (2)\end{matrix}$

where y_(i) and y_(i) are the actual class and the predicted class,respectively. The optimizer used for our experiments is Adam with alearning rate of 1×10⁶ for 40 epochs.

In the study, DeepSymNet was trained and tested using a 10-foldcross-validation on 2 binary variables: presence/absence of LVO and adichotomized ischemic core size with a threshold of 30 or 50 mL. TheDeepSymNet probabilities for the 2 binary variables were then used forthe statistical analysis. The contribution of each voxel toward reachingthe output classifier probability was evaluated using ϵ-layerwiserelevance propagation.

Univariate comparisons between continuous variables were performed usinga Student's t test or Wilcoxon rank-sum testing for abnormal data, andunivariate comparisons of categorical variables were performed using theFisher exact test. From the DeepSymNet probabilities described above forthe 3 comparisons (LVO detection, ischemic core ≤30 mL, and ischemiccore ≤50 mL), receiver-operator curve analysis was performed, andDeepSymNet determinations were evaluated using AUC measurements with 95%confidence intervals (Cis). Correlation between DeepSymNet probabilitiesand CTP-RAPID® ischemic core volume predictions (as a continuousvariable) was performed using Pearson correlation. The data is presentedas mean±SD or median (interquartile range) unless otherwise specified.Analyses were performed using the Scikit-Learn/Statsmodels Pythonlibraries and confirmed with Stata/MP 14 (StataCorp LLC, CollegeStation, Tex.), Prism 7 (GraphPad, La Jolla, Calif.) statisticalsoftware.

Among the 297 patients who met the inclusion criteria for the study, themedian age was 67 and 50% were female. 224 total patients ultimatelydiagnosed with AIS were included, and 73 who underwent an acute strokeevaluation but did not have AIS. The non-stroke subgroup was younger (63versus 69; P<0.01). The proportion of patients with LVO wasintentionally enriched in this population, as described above, and LVOwas present in 179 patients (60%). Among patients diagnosed with AIS,the occlusion location was intracranial (ICA) in 13%, M1 middle cerebralartery (MCA) in 44%, and M2 MCA in 21%. Median ASPECTS was 9 (7-10).Among patients with AIS, 157 (70%) presented within the early timewindow (≤6 hours) and the rest presented in the late time window(between 6 and 24 hours). Among patients with LVO, 124 (69%) were foundon CTP-RAPID® imaging to have ischemic core volume of ≤30 mL and 143(80%) ≤50 m L.

Using this dataset, the trained DeepSymNet was then used to evaluate theregions on the images that lead to the algorithm's decisions. FIG. 7demonstrates the average activation zones leading to decision making ina DeepSymNet model trained on discriminating between LVO and non-LVO forthe full cohort. The model was strongly activated by the regions of theimage corresponding to the vasculature.

The performance of DeepSymNet predictions was then evaluated across theentire cohort (LVO AIS, non-LVO AIS, and stroke mimics). Examined firstwas the ability of the algorithm to discriminate between LVO and non-LVO(non-LVO AIS and stroke mimic). As shown in FIG. 8, the model performedwith AUC 0.88 (0.83-0.92; 95% CI) for the full cohort and AUC 0.84(0.79-0.9; 95% CI) for LVO AIS and non-LVO AIS.

The performance of the algorithm was next evaluated across the entirecohort to identify if the ischemic core as predicted by CTP-RAPID® was≤30 and 50 mL (DAWN Criteria 1 and DAWN Criteria 2). As shown in FIG.9A, the algorithm performed similarly (AUC 0.88 [0.82-0.92, 95% CI] and0.90 [0.82-0.96, 95% CI], DAWN Criteria 1 and DAWN Criteria 2).DeepSymNet discrimination performance of large versus small ischemiccores increased as a function of the ischemic core threshold used, asshown in FIG. 9B.

In sensitivity analysis, the accuracy of DeepSymNet to predict ischemiccore volume thresholds in patients presenting in the early and late timewindow was then separately determined. As shown in FIG. 9C, in the earlytime window, the performance of DeepSymNet remained stable (AUC 0.87 and0.90, DAWN Criteria 1 and DAWN Criteria 2). This finding was alsomaintained in the subset of patients presenting in the late time window(AUC 0.89 and 0.91, DAWN Criteria 1 and DAWN Criteria 2), as shown inFIG. 9D.

While DeepSymNet was designed to identify dichotomous thresholds, theprobabilities can be evaluated as continuous variables and correlated tocontinuous ischemic core measurements by CTP-RAPID®. DeepSymNetprobabilities had an acceptable correlation against CTP-RAPID®predictions of ischemic core (r=0.70 [0.63-0.75]; P<0.0001, Pearsoncorrelation).

The above results demonstrate that the DeepSymNet algorithm exhibitshigh accuracy in identifying patients with LVOs as well as dichotomizedmeasures of ischemic core, with AUCs greater than the threshold of 0.8to define good performance in prior imaging biomarker studies. Thealgorithm autonomously learned to use disease-relevant areas to generateits predictions without any prior knowledge, building confidence in themethodology. These results demonstrate that much of the data needed toperform the neuroimaging evaluation for EST may be present in readilyavailable imaging modalities, such as non-contrast head CT and CTA, aswell as the potential for machine-learning approaches to automate theseanalyses.

While the foregoing disclosure has focused on obtaining results thatassist a medical professional in determining whether or not EST shouldbe performed, it is noted that the disclosed systems and methods canmore generally be used to evaluate the brain after onset of a strokewhether or not the information gleaned from the evaluation is to be usedto make an EST eligibility determination. Accordingly, the systems andmethods can be used to evaluate a stroke patient's brain insubstantially any context.

1. A method for evaluating the brain after onset of a stroke, the methodcomprising: capturing computed tomography angiography (CTA) images of apatient's brain; analyzing the CTA images with a CTA image analysisprogram to evaluate the patient's brain; and generating results basedupon the analysis that provide an assessment of the patient's brain. 2.The method of claim 1, wherein capturing CTA images comprises capturingthe CTA images within 24 hours of the patient experiencing the onset ofstroke symptoms.
 3. The method of claim 1, wherein analyzing the CTAimages comprises determining an extent of an existing infarction.
 4. Themethod of claim 1, wherein analyzing the CTA images comprisesdetermining an amount of brain tissue at risk of infarction.
 5. Themethod of claim 1, wherein analyzing the CTA images comprisesdetermining the presence or absence of large vessel occlusion.
 6. Themethod of claim 1, wherein analyzing the CTA images comprisesdetermining the presence or absence of brain hemorrhage.
 7. The methodof claim 1, wherein generating results comprises presenting anindication as to whether or not endovascular stroke therapy is advisablefor the patient.
 8. The method of claim 1, wherein the CTA imageanalysis program comprises a machine-learning algorithm that has beentrained on results of perfusion imaging analysis.
 9. The method of claim8, wherein the machine-learning algorithm comprises a convolutionalneural network.
 10. The method of claim 9, wherein the convolutionalneural network is configured to compare the two hemispheres of thepatient's brain both in the presence and in the absence of blood vesselsin the CTA images to identify and characterize the patient's brain. 11.The method of claim 10, wherein the convolutional neural networkcomprises a merge layer that calculates an absolute difference betweenhigh-level convolution filter outputs common to the two hemispheres,which provides information as to the asymmetry of the two hemispheres.12. The method of claim 1, further comprising, prior to analyzing theCTA images, processing the CTA images to remove undesired anatomicalfeatures from the images.
 13. The method of claim 1, further comprising,prior to analyzing the CTA images, registering the CTA images to a brainatlas.
 14. A non-transitory computer-readable medium that stores acomputed tomography angiography (CTA) image analysis program comprisingcomputer-executable instructions configured to: receive CTA images of apatient's brain; analyze the CTA images to evaluate the patient's brain;and generate results based upon the analysis that provide an assessmentof the patient's brain.
 15. The non-transitory computer-readable mediumof claim 14, wherein the CTA image analysis program is configured todetermine an extent of an existing infarction.
 16. The non-transitorycomputer-readable medium of claim 14, wherein the CTA image analysisprogram is configured to determine an amount of brain tissue at risk ofinfarction.
 17. The non-transitory computer-readable medium of claim 14,wherein the CTA image analysis program is configured to determine thepresence or absence of large vessel occlusion.
 18. The non-transitorycomputer-readable medium of claim 14, wherein the CTA image analysisprogram is configured to determine the presence or absence of brainhemorrhage.
 19. The non-transitory computer-readable medium of claim 14,wherein the CTA image analysis program comprises a machine-learningalgorithm that has been trained on results of perfusion imaginganalysis.
 20. The non-transitory computer-readable medium of claim 19,wherein the machine-learning algorithm comprises a convolutional neuralnetwork.
 21. The non-transitory computer-readable medium of claim 20,wherein the convolutional neural network is configured to compare thetwo hemispheres of the patient's brain both in the presence and in theabsence of blood vessels in the CTA images to identify and characterizethe brain.
 22. The non-transitory computer-readable medium of claim 21,wherein the convolutional neural network comprises a merge layer thatcalculates an absolute difference between high-level convolution filteroutputs common to the two hemispheres, which provides information as tothe asymmetry of the two hemispheres.
 23. The non-transitorycomputer-readable medium of claim 14, further comprising, prior toanalyzing the CTA images, processing the CTA images to remove undesiredanatomical features from the images.
 24. The non-transitorycomputer-readable medium of claim 14, further comprising, prior toanalyzing the CTA images, registering the CTA images to a brain atlas.